Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care

Abstract Background With the ever‐increasing integration of artificial intelligence (AI) into health care, it becomes imperative to gain an in‐depth understanding of how health care professionals, specifically nurse practitioners, perceive and approach this transformative technology. Objectives This study aimed to gain insights into nurse practitioners' perceptions and attitudes toward AI adoption in health care. Methods This qualitative research employed a descriptive and phenomenological approach using in‐depth interviews. Data were collected through a semi‐structured questionnaire with 37 nurse practitioners selected through purposive sampling, specifically Maximum Variation Sampling and Expert Sampling techniques, to ensure diversity in characteristics. Trustworthiness of the research was maintained through member checking and peer debriefing. Thematic analysis was employed to uncover recurring themes and patterns in the data. Results The thematic analysis revealed nine main themes that encapsulated nurse practitioners' perceptions and attitudes toward AI adoption in health care. These included nurse practitioners' perceptions of AI implementation, attitudes toward AI adoption, patient‐centered care and AI, quality of health care delivery and AI, ethical and regulatory aspects of AI, education and training needs, collaboration and interdisciplinary relationships, obstacles in integrating AI, and AI and health care policy. While this study found that nurse practitioners held a wide range of perspectives, with many viewings AI as a tool to enhance patient care. Conclusions This research provides a valuable contribution to the evolving discourse surrounding AI adoption in health care. The findings underscore the necessity for comprehensive education and training in AI, accompanied by clear and robust ethical and regulatory guidelines to ensure the responsible integration of AI in health care practice. Furthermore, fostering collaboration and interdisciplinary relationships is pivotal for the successful incorporation of AI in health care. Policymakers should also address the challenges and opportunities that AI presents in the health care sector. This study enhances the ongoing conversation on AI adoption in health care by shedding light on the perspectives of nurses, thereby shaping future strategies for AI integration.

Artificial Intelligence (AI) represents the pinnacle of technological innovation, enabling machines to perform tasks that typically require human intelligence, such as learning and decision-making. 1 By utilizing advanced algorithms and data processing, AI systems can swiftly analyze large volumes of data to derive meaningful insights, adapting and improving their performance over time. 2,3This technology has applications across diverse fields, from natural language processing to machine learning, and is transforming industries and daily life. 4,5 nursing, AI demonstrates significant potential for enhancing patient outcomes through predictive analytics, personalized treatment plans, and early health deterioration detection. 6AI-enhanced electronic health records (EHRs) improve the efficiency of patient data management, reducing documentation time and allowing nurses to concentrate more on patient care. 7Moreover, AI's integration into nursing education via simulation and virtual reality shows promise in advancing clinical skills and decision-making. 8AI also plays a role in nursing research by automating literature reviews and thematic analyses, which enhances research efficiency and accuracy. 9 health care, AI is revolutionizing medical data processing, diagnostics, and treatment administration. 10Machine learning algorithms within AI systems improve clinical decision-making, predict patient outcomes, 11 and automate administrative tasks. 12,13These technologies address persistent health care challenges such as diagnostic errors and treatment optimization. 14AI-integrated EHRs support medical data analysis and predictive analytics, 15 while telemedicine platforms use AI tools like chatbots and virtual health assistants to streamline patient communication and aid in diagnosing common conditions. 16,17The rapid advancement of AI tools, including diagnostic aids and robotic surgical systems, is reshaping the health care landscape. 18rse practitioners are vital to health care delivery, serving as integral members of interdisciplinary teams and often providing primary care services. 19,20They handle patient assessments, diagnoses, treatments, and chronic condition management. 21sides their clinical duties, nurse practitioners educate and mentor, ensuring holistic and patient-centered care. 22Their extensive patient interactions provide deep insights into patient needs and preferences, 23 and they bridge the gap between patients and health care professionals, translating advancements like AI into practical care. 24Thus, their attitudes towards AI are crucial for the successful integration of these technologies in clinical settings. 25obally, international collaboration is essential for advancing AI in health care.Sharing resources and knowledge accelerates the development of medical AI applications. 26Ethical guidelines from international organizations aim to address issues of bias, transparency, and privacy in AI development. 27Investments in education and the development of regulatory frameworks are necessary to balance innovation with safeguards. 28,29National strategies, such as those in Bangladesh's High-Tech Park, emphasize advanced infrastructure and international partnerships to drive health care AI innovation. 30,31wever, AI adoption faces challenges, including data privacy concerns, system interoperability issues, and resistance from health care professionals. 32,33Understanding health care professionals' perspectives on AI is crucial, yet there is limited research in this area. 34,357][38] Ultimately, this study aimed to shed light on nurse practitioners' perceptions and attitudes regarding the adoption of AI in health care, exploring how these professionals perceive the evolving health care technology landscape and its impact on their practice.

| Research paradigm
In this study, the research paradigm was grounded in the constructivist paradigm. 39This paradigm acknowledged that individuals actively constructed their own realities and that their understanding of these realities was inherently subjective. 40Within this paradigm, the focus had been on the diverse perspectives and interpretations of the participants, which proved particularly relevant when exploring their perceptions and attitudes towards the incorporation of AI in health care. 41The study followed the guidelines of the Consolidated Criteria for Reporting Qualitative Research by Tong et al. ensuring rigorous reporting and transparency in qualitative research methodology. 42

| Theoretical framework
This study utilized the Technology Acceptance Model (TAM) as its theoretical framework. 43TAM is a widely used model that helps in understanding how users come to accept and use a technology.It posits that perceived usefulness and perceived ease of use are primary factors that influence users' attitudes towards adopting new technologies. 8By applying TAM, the study aimed to systematically explore the factors influencing nurse practitioners' perceptions and attitudes towards AI adoption in health care settings.This framework provided a structured approach to analyze the data, offering insights into the potential facilitators and barriers to AI adoption from the perspective of nurse practitioners.

| Research approach
An exploratory, descriptive, and phenomenological research approach was employed in the study. 44This approach enabled a comprehensive examination of the lived experiences and perceptions 45 of nurse practitioners regarding AI in health care.The exploratory aspect had been essential in uncovering the diversity of perceptions and experiences, while the descriptive nature ensured a detailed account of these experiences. 46The phenomenological approach had delved deep into the essence of these experiences, shedding light on the underlying meanings that nurse practitioners attached to AI adoption.

| Data collection
The data were primarily collected between June 10 and July 25, 2023, through semi-structured questions.This implies that the participants were given a certain degree of flexibility in responding while ensuring that there was a basic structure or a set of predefined questions guiding the interviews. 47The interviews took place faceto-face or via video conferencing for 50−60 min, depending on participant preference and availability.A semi-structured format allowed for open-ended questions while ensuring consistency in data collection. 48The interview guide was developed based on a thorough literature review, piloted among five participants, and some questions were reworded based on participants' comments.The interviews were audio-recorded with participant consent and transcribed verbatim for analysis.Audio recording ensures a reliable record of the conversations and allows for a more accurate analysis of the data. 49anscribed verbatim means that the recorded conversations were converted into written text without any alteration. 50The interview questions included:

| Participant selection
A purposive sampling technique, specifically Maximum Variation Sampling and Expert Sampling, was employed to ensure the selection of participants with diverse characteristics. 45The inclusion of participants with diverse characteristics such as age, gender, highest degree, marital status, working experience, and employee type (Table 1) suggests the use of Maximum Variation Sampling.This approach ensures a broad representation of perspectives among nurse practitioners, enriching the data by capturing a wide spectrum of viewpoints. 51milarly, the specified criteria for participant selection, including having a minimum of 5 years of clinical experience and possessing 3 months of clinical experience with AI or attending a minimum of three conferences on AI adoption in health care, indicate the use of Expert Sampling.This method targets individuals with specialized knowledge or expertize 52 in the field of health care and AI adoption.By including participants who meet these criteria, the researcher ensures that the sample consists of individuals with relevant experience and insights, contributing to a more nuanced understanding of the subject matter.
Additionally, recruitment was conducted through professional networks, health care organizations, and nursing associations.Fifty-nine potential participants were contacted via email or phone calls to explain the study's purpose, obtain informed consent, and schedule interviews.Among them, 37 nurse practitioners from five tertiary-level health care institutions in Dhaka, Bangladesh, were recruited.

| Trustworthiness
Ensuring the credibility and transferability of the research findings was of utmost importance, prompting the implementation of essential strategies. 53These included member checking and peer debriefing, each serving a distinct purpose in fortifying the robustness of the study. 54mber checking emerged as a pivotal step in maintaining the accuracy and interpretive integrity of the findings. 55By affording participants the opportunity to scrutinize a summary of their interview responses, it enabled them to confirm the precision and interpretation of their contributions, thereby ensuring the faithful representation of their viewpoints in the final research findings. 56This meticulous validation process was integral to the overall credibility of the research.Complementing this, peer debriefing stood as another indispensable element. 57The research team, through routine engagements in discussions, aimed to deliberate on the findings and interpretations collaboratively.These tailored sessions were crafted to minimize potential biases and enhance the overall credibility of the analysis, leveraging the diverse perspectives within the team. 58In concert, member checking and peer debriefing formed a comprehensive framework, fortifying the trustworthiness and applicability of the research findings.

| Thematic analysis
The qualitative data, gathered through in-depth interviews, underwent a rigorous and structured process of thematic analysis to uncover recurring themes, patterns, and categories within the data set. 52This analytical journey unfolded through a series of sequential stages, each contributing to the overall understanding of nurse practitioners' perceptions and attitudes towards the adoption of AI in health care.At the outset, the data familiarization stage entailed a comprehensive review of the interview transcripts, involving multiple iterations to foster a profound grasp of the content. 59This meticulous approach enabled the research team to deeply immerse themselves in the data and glean rich insights from the participants' perspectives.Subsequently, the analysis evolved into generating initial codes (Tables 2-6).During this phase, segments of text directly pertinent to the research question were systematically coded, meticulously extracting and emphasizing key elements within the data set. 60e following stage of analysis revolved around theme development.Here, four researchers carefully identified initial codes and potential themes.Simultaneously, the analysis process involved an ongoing review and refinement of the identified themes, ensuring their accuracy and cohesiveness 61 (Figure 1).Where necessary, sub-themes were developed to capture nuanced aspects of the data comprehensively.In the final stage of this analytical journey, the research findings were meticulously presented in a clear and coherent manner, bolstered by direct quotes from the participants (Tables 2-6).This reporting phase marked the culmination of the analytical process, providing a solid foundation for understanding nurse practitioners' perceptions and attitudes regarding the integration of AI in health care.

| Ethical considerations
This research rigorously adhered to ethical guidelines, ensuring the protection of participants' rights and well-being. 62Participants received a comprehensive consent form outlining the study's objectives, procedures, and their rights, including the freedom to withdraw at any time without negative consequences.Anonymity and confidentiality were strictly maintained, with all data securely stored to prevent unauthorized access. 63After their participation, individuals were offered debriefing sessions to discuss their experiences and address any concerns. 64 Initially skeptical, practitioners now value AI for streamlining tasks, reducing administrative burdens, and improving patient care, highlighting its growing role in their health care toolkit (Table 2).

| Attitudes toward AI adoption
Nurse practitioners in this study warmly embraced AI, viewing it as transformative for personalized treatment and patient care.They valued AI as a virtual health companion that improves communication and streamlines health care delivery.However, concerns about dehumanization, data security, and job displacement were noted.
Practitioners emphasized AI should complement, not replace, their roles and called for vigilance against biases.They advocated for clear guidelines to align AI with health care regulations, ensuring ethical integration and addressing potential disparities.Despite these concerns, AI was welcomed as a valuable tool for enhancing health care solutions (Table 3).

| Patient-centered care and AI
Participants in the study stressed the importance of maintaining a personal connection in health care, highlighting the irreplaceable human touch, empathy, and trust provided by health care providers.
While acknowledging AI's benefits, they emphasized that AI cannot replace these critical elements of patient care.The study underscored the need for a balance where AI enhances rather than overshadows the personal connection valued by patients.Nurse practitioners also care and data analysis is seen as crucial for early intervention and patient well-being (Table 4).

| Ethical and regulatory aspects of AI
Nurse practitioners highlighted the critical importance of data security and privacy in AI use.They stressed the need for vigilance, encryption, and access controls to protect patient information, linking these measures to broader ethical and trust issues.Practitioners acknowledged the complexities of obtaining informed consent, emphasizing the need for comprehensive patient education and transparent dialogues about AI's role and implications.They also stressed the importance of addressing biases and ensuring equity in AI adoption to provide all patients with fair access and outcomes (Table 5).5).

| Collaboration and interdisciplinary relationships
In this research, nurse practitioners highlighted the benefits of collaborating with AI specialists, recognizing their technical expertize as vital for improving patient care.This partnership merges technical skills with a patient-centered approach, introducing transformative tools that enhance health care delivery.Nurse practitioners also stressed their essential role in AI implementation, ensuring technology aligns with patient needs.Their involvement is crucial for developing patient-centric AI solutions, thus aligning with health care's core mission to promote patient well-being.This collaboration strategy is key to maximizing AI's benefits in health care (Table 6).

| Obstacles in integrating AI
Nurse practitioners faced challenges integrating AI into their established systems, stressing the need for careful planning to avoid workflow disruptions.They emphasized the importance of seamless integration with existing practices to maintain a patient-focused  6).

| AI and health care policy
Nurse practitioners emphasized the crucial role of flexible government regulations in health care AI.Collaboration between policymakers and health care professionals is vital to balance innovation and patient safety.Furthermore, they underscored the need for strong AI policies within health care institutions.These policies offer clear ethical guidelines, ensuring transparency, trust, and adaptability in the face of evolving technology.In addition, nurse practitioners highlighted the significance of equitable reimbursement policies for ethical AI integration in health care.These policies should provide incentives for AI usage, align with ethical principles, and ultimately enhance patient care and outcomes (Table 6).

| DISCUSSION
This study presents a nuanced view of nurse practitioners' perceptions and attitudes toward AI in health care, encompassing their perspectives on implementation, impact, ethics, education, and integration challenges.This discourse underscores the evolving Ensuring equity in AI use "Equity matters in AI adoption.We must address biases to ensure fair treatment for all patients, regardless of their background (NP31)" "AI should enhance health care for everyone.We must actively work to eliminate biases and disparities in its application (NP9)" "AI has the potential to worsen disparities.We need to actively strive for fairness, ensuring equal access and outcomes for all (NP24)" Equity, AI, Bias, Fair | 13 of 21 relationship between health care professionals and AI, highlighting both the opportunities and obstacles that AI introduces into the health care domain.
A notable finding from the study is the shift in nurse practitioners' attitudes towards AI, especially in its diagnostic role.They recognize AI's potential to process vast amounts of patient data and identify patterns that can enhance diagnostic accuracy.This aligns with existing research indicating that AI complements, rather than replaces, clinical judgment. 35,65The growing acceptance of AI in diagnostics reflects a broader trend towards integrating AI into health care decision-making processes. 66,67Nurse practitioners increasingly view AI as a valuable asset in treatment decision-making, offering personalized recommendations based on patient-specific data.This acceptance is supported by other studies demonstrating AI's ability to improve treatment quality through evidence-based guidance 68,69 and its integration into clinical decision support systems. 70's role extends to facilitating nursing care planning by analyzing patient data to predict complications and suggest individualized interventions. 71This capability is seen as a significant enhancement to personalized care and effective management of complex cases.In patient communication, AI is viewed as a virtual health companion that can provide information, answer questions, and support patient education.This perspective is supported by research highlighting AI's potential to improve patient engagement and education. 72,73e study also reveals that nurse practitioners appreciate AI's potential to streamline workflows and reduce administrative burdens, aligning with studies that demonstrate AI's effectiveness in optimizing health care processes. 74,75Additionally, AI-driven predictive analytics are recognized for their potential to improve patient care outcomes through data-driven insights. 28However, despite these positive aspects, nurse practitioners' express concerns about the dehumanization of care, job displacement, and bias associated with AI.These concerns echo the broader apprehensions about AI in health care, emphasizing the need for responsible AI integration that complements rather than replaces human roles. 38,76,77e balance between technology and human interaction is a key theme.Nurse practitioners emphasize the importance of maintaining a personal connection with patients while using AI as a supportive tool, aligning with literature on preserving the human touch amidst technological advancements. 78,79Effective communication about AI's role is crucial for maintaining transparency and patient-centered care. 80,81e study also highlights AI's role in improving health care delivery, with nurse practitioners recognizing its impact on reducing diagnostic errors, enhancing treatment planning, and improving care coordination.This is consistent with research affirming AI's potential to enhance efficiency and safety in health care. 82,83AI's continuous monitoring capabilities are praised for their role in patient safety, 84,85 and its potential in preventive care by identifying risk factors is also noted.
Ethical and regulatory considerations are paramount.Nurse practitioners emphasize the importance of data security, privacy, and informed consent in AI use, aligning with the broader emphasis on protecting patient information. 86They advocate for comprehensive education and training on AI, noting the need for up-to-date courses covering AI fundamentals, applications, and ethics. 87,88Collaboration between nurse practitioners and AI specialists is seen as vital for enhancing decision-making and patient care, stressing the need for interdisciplinary approaches. 89,90e study also addresses obstacles to AI integration, such as workflow disruptions, technical barriers, and financial constraints.
Nurse practitioners call for meticulous planning and costeffective solutions to facilitate smooth transitions.Furthermore, they highlight the need for flexible government regulations and ethical guidelines to ensure responsible AI integration in health care institutions. 91

| IMPLICATIONS OF THIS STUDY
This study has significant implications for health care.It reveals that nurse practitioners' views on AI integration are varied, with some enthusiastic and others concerned.This calls for tailored strategies to educate and engage practitioners on AI's benefits and risks.Addressing their concerns about AI exacerbating health care inequality is crucial; AI systems must be designed to ensure

3. 6 |
Main theme: Education and training needs Nurse practitioners advocated for integrating AI into health care education, emphasizing the need for comprehensive courses on AI fundamentals, applications, and ethics.They highlighted that including AI in curricula bridges theory and practice, equipping future professionals with essential skills.Practitioners called for tailored training programs that address real-world AI applications and support professional development.They also recognized the challenges of keeping up with rapidly evolving AI technology, stressing the importance of continuous learning and institutional support to balance clinical duties with acquiring AI expertize (Table In your opinion, what are the most significant advantages that AI can offer in health care, and how do you envision it improving patient care? 4. What concerns or challenges did you associate with the adoption of AI in health care, and how did you think these issues could be Were there particular areas or aspects of health care where you thought AI could make the most significant impact, and why? 9. How did you see the future of health care evolving with the increasing presence of AI, and what role did you think nurse practitioners would play in this changing landscape?
1. Could you describe your experiences with AI in health care and how it impacted your role as a nurse practitioner?2. What were your initial thoughts and feelings about the integration of AI into your daily clinical practice?3.
T A B L E 1 Characteristics of participants.
Main themes, sub-themes, participants' quotes, and codes."Ibelievegovernment regulations play a pivotal role in guiding the ethical use of AI in health care.They must be agile and adaptive, capable of keeping pace with AI advancements.A strong collaboration between health care professionals and policymakers is crucial to ensure that AI is harnessed for the betterment of patient care (NP7)" AI policies within health care institutions are indispensable.They provide clear guidelines and establish a framework for the responsible use of AI technologies.These policies help in ensuring transparency and maintaining patient trust while fostering a culture of ethical AI adoption within the organization (NP21)" "Health care institutions require robust AI policies that set clear boundaries and ethical standards.These policies help maintain transparency and accountability, ensuring that AI benefits both patients and providers.Reimbursement policies should reflect the responsible integration of AI in health care.These policies must incentivize health care providers to utilize AI for quality patient care.Fair compensation for AI adoption aligns with ethical AI practices and encourages its responsible use (NP23)" "AI's role in insurance and reimbursement needs to ensure that health care providers are fairly compensated for the use of AI.These policies should not only encourage the ethical adoption of AI but also benefit patients through improved health care outcomes (NP32)" Reimbursement policies, Responsible integration, Health care, Incentivize, Health care providers, Quality patient care, Fair compensation, Ethical AI practices, Responsible use, Insurance, Benefit patients, Improved health care outcomes perspectives.Additionally, relying solely on semi-structured interviews may miss insights that surveys or observations could provide.The study also did not explore the impact of the COVID-19 pandemic on attitudes toward AI adoption.Furthermore, it did not fully address challenges related to AI implementation, such as data privacy, algorithm bias, and regulatory issues.7 | RECOMMENDATIONS FOR FUTURE RESEARCHFuture research on nurse practitioners' views on AI in health care should include several key areas.Longitudinal studies are needed to track how perceptions and attitudes evolve as AI becomes integral to practice, identifying trends, barriers, and opportunities.Exploring ethical issues, such as privacy, consent, transparency, and fairness, is essential to understand how practitioners navigate these dilemmas.Quantitative research should assess AI's impact on clinical decision-making, diagnostic accuracy, and patient outcomes.Investigating how AI can be tailored to meet patient preferences and values is crucial for maintaining patientcentered care.Evaluating the effectiveness of AI education and training programs will gauge their impact on practitioners' readiness and skill levels.Additionally, research should focus on overcoming challenges related to AI integration, including workflow disruptions, technical issues, and financial constraints.Finally, examining the influence of government regulations and institutional policies will help identify necessary adjustments for responsible AI adoption.feelings about its impact on workflow, predictive analytics, and patient outcomes.Trust in AI decision support is crucial, necessitating confidence-building among health care professionals.Attitudes vary, with concerns about health care inequality and regulatory compliance.Ethical considerations, such as privacy and data security, also influence willingness to adopt AI.The study highlights the need for a patient-centered approach, effective communication, and overcoming technical and financial barriers.Education and training in AI are essential, with a call to integrate AI into educational programs.Health care policy plays a pivotal role in AI adoption, influencing regulations, insurance, and reimbursement.Overall, a comprehensive approach is needed to address diverse challenges and enhance health care quality while considering ethical and regulatory concerns.
and informed consent in AI use.Clear regulations will help build trust in AI systems.Finally, nurse practitioners are vital for maintaining patient connections and explaining AI use.They will need specialized training to effectively balance technology with personal care and support patients throughout their health care journey.6 | LIMITATIONS OF THIS STUDYThis study on nurse practitioners' perceptions of AI in health care has several limitations.First, its geographical focus on Dhaka, Bangladesh, may limit generalizability to other regions with different health care systems and cultures.The purposive sampling method, while ensuring diversity, may introduce bias due to specific inclusion criteria, such as prior AI experience or conference attendance, potentially skewingT A B L E 6 (Continued)8 | CONCLUSIONSThe study on nurse practitioners' perceptions of AI adoption in health care reveals a complex array of opinions and concerns.Key themes include AI's role as a valuable tool for diagnosis and treatment, but with mixed